Artificial intelligence–derived immune phenotypes for prediction of prognosis in patients with stage III colon cancer (NCCTG N0147; Alliance).
Authors: Bahar Saberzadeh Ardestani, Garth D. Nelson, Diana I Segovia, Yoojoo Lim, Dongyao Yan, Kandavel Shanmugam, Steven R Alberts, Gahee Park, Qian Shi, Chan-Young Ock, Frank A. Sinicrope
Journal: Journal of Clinical Oncology (2023)

3542 Background: Solid tumors can be characterized by distinct immune phenotypes based on the level of T cell infiltration in the tumor microenvironment (TME) which may provide prognostic information and also inform strategies to restore the anti-tumor immune response. Immune phenotypes were determined using an artificial intelligence (AI) algorithm in digitized whole-slide images (WSI) of tumors with deficient (d) mismatch repair (MMR) vs proficient (p) MMR. Methods: Stage III colon carcinomas (N = 401; 393 met QC) from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy were analyzed including all available tumors with dMMR (n = 196) and a randomly selected cohort of pMMR tumors (n = 195). Using an AI algorithm (Lunit SCOPE) previously trained on solid tumors, digitized tumors were categorized into three immune phenotypes based on epithelial and stromal TIL data-driven cutpoints in dMMR and pMMR (inflamed: TIL high in epithelium (&gt; upper 25%); excluded: TIL high in stroma (&gt;lower 25%), low in epithelium; and desert: TIL low in epithelium and stroma). Phenotypes were then examined in relationship to disease-free survival (DFS) using Kaplan-Meier methodology. Results: Of the 3 immune phenotypes in dMMR tumors, 25% were inflamed, 50% were immune-excluded, and 25% were immune-desert. Based on univariate results, multivariable modeling was performed incorporating immune phenotype in addition to age, histological grade, N stage, T stage, performance status, treatment arm, BRAF and KRAS status and identified immune phenotypes and N stage to be significantly associated with DFS. Among dMMR tumors, immune desert phenotype was associated with the poorest DFS (Desert: HR adj 1.95, 95%CI (1.01, 3.80); Excluded: HR adj 0.89, 95%CI (0.47, 1.69); Inflamed: ref; p:0.01). When compared to immune desert tumors, immune-excluded tumors had significantly better DFS (HR adj 0.49, 95%CI (0.27, 0.88), p:0.01). At a median follow-up of 60 months, 3-year DFS of patients with dMMR was 71.2% with Immune inflamed, 78.5% with Excluded, and 54.5% with Desert phenotypes. In contrast to dMMR, univariate analyses of data-driven cutpoints in pMMR tumors was not prognostic. Using a different data-driven cutpoint (15%), the revised immune phenotypes remained non-significant for DFS within pMMR tumors. Conclusions: Distinct AI-derived immune phenotypes in the TME were identified that were significantly prognostic in patients with dMMR, but not pMMR colon cancers. A data-driven immune-desert phenotype was identified in dMMR tumors that was associated with significantly poorer survival. Further investigation of the potential predictive utility of these phenotypes for immunotherapy are planned. Support: https://acknowledgments.alliancefound.org . ClinicalTrials.gov Identifier: NCT00079274 .